Online Bidding Algorithms for Return-on-Spend Constrained Advertisers

Understand

Online advertising has recently grown into a highly competitive and complex multi-billion-dollar industry, with advertisers bidding for ad slots at large scales and high frequencies.

• This has resulted in a growing need for efficient "auto-bidding" algorithms that determine the bids for incoming queries to maximize advertisers' targets subject to their specified constraints.
• This work explores efficient online algorithms for a single value-maximizing advertiser under an increasingly popular constraint: Return-on-Spend (RoS).
• We quantify efficiency in terms of regret relative to the optimal algorithm, which knows all queries a priori.